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This paper introduces a novel approach for designing a computationally effective type-2 fuzzy logic controller. For this purpose, on the antecedent side, interval type-2 fuzzy sets are employed to capture the signal readings, which significantly reduce the computation costs while preserving the major advantages of general type-2 fuzzy logic systems. On the consequent side, however, the Takagi-Sugeno-Kang (TSK) technique is integrated with the proposed controller to render the control outputs in a parallel way. To further reduce the computation cost, the theory of uncertainty bounds is employed for the output processing of the proposed controller. To develop this control structure, a decomposition technique is integrated to break down the original type-2 fuzzy processes into type-1 and take advantage of type-1 fuzzy techniques, followed by an aggregation mechanism to calculate the collective output. The approach is applied to the control of an inverted pendulum and cart model. The simulation results of the developed interval type-2 fuzzy logic controller is compared with a type-1 TSK fuzzy logic controller and a classical proportional derivative (PD) controller. From the results, we have found a 16.6% and 23.3% improvement in Root Mean Square (RMS) error compared to type-1 TSK fuzzy logic controller and classical PD controller, respectively.<\/jats:p>","DOI":"10.3233\/jifs-190446","type":"journal-article","created":{"date-parts":[[2019,12,31]],"date-time":"2019-12-31T07:39:35Z","timestamp":1577777975000},"page":"1915-1928","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":11,"title":["Developing a computationally effective Interval Type-2 TSK Fuzzy Logic Controller"],"prefix":"10.1177","volume":"38","author":[{"given":"Abel","family":"Hailemichael","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC, USA"}]},{"given":"Syed Moshfeq","family":"Salaken","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation, Deakin University, Victoria, Australia"}]},{"given":"Ali","family":"Karimoddini","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC, USA"}]},{"given":"Abdollah","family":"Homaifar","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC, USA"}]},{"given":"Khosravi","family":"Abbas","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation, Deakin University, Victoria, Australia"}]},{"given":"Saeid","family":"Nahavandi","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation, Deakin University, Victoria, Australia"}]}],"member":"179","published-online":{"date-parts":[[2019,12,26]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/0020-0255(75)90036-5"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/0020-0255(75)90046-8"},{"key":"e_1_3_1_4_2","first-page":"915","article-title":"Introduction to type-2 fuzzy logic systems","volume":"2","author":"Karnik N.N.","year":"1998","unstructured":"KarnikN.N. and MendelJ.M., Introduction to type-2 fuzzy logic systems, IEEE World Congress on Computational Intelligence 2 (1998), 915\u2013920.","journal-title":"IEEE World Congress on Computational Intelligence"},{"key":"e_1_3_1_5_2","unstructured":"CastilloO. 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